1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
|
/*
selfplay: generate training data
Copyright (C) 2026, tslil clingman
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with ctak. If not, see <https://www.gnu.org/licenses/>.
*/
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <time.h>
#include <unistd.h>
#include "actions.h"
#include "negamax.h"
#include "pv.h"
#include "tak.h"
#include "xorshift64.h"
#ifndef RECORD_PV
#error "selfplay must be built with -DRECORD_PV (see the selfplay Makefile target)"
#endif
#define MAX_PLY 255
#define MAX_CELLS (6 * 6)
static tak_state_p state;
static float row_head[MAX_PLY][3];
static uint8_t row_cells[MAX_PLY][MAX_CELLS];
static uint8_t row_term[MAX_PLY];
static float row_terminal_value[MAX_PLY];
static uint32_t row_game_id[MAX_PLY];
static uint32_t n_rows;
static uint32_t game_id_black;
static uint32_t game_id_white;
void negamax_display_progress(const uint8_t cur_depth,
const uint8_t init_depth,
const uint32_t length) {
(void)(cur_depth);
(void)(init_depth);
(void)(length);
}
static float win_value(const enum WIN_TYPE w) {
switch (w) {
case WIN_FLAT_BLACK:
case WIN_ROAD_BLACK:
return 1.0f;
case WIN_FLAT_WHITE:
case WIN_ROAD_WHITE:
return -1.0f;
default:
return 0.0f;
}
}
static int play_game(const uint8_t min_ply) {
reset_state(state, state->board_size);
n_rows = 0;
char ptn[16];
for (uint32_t ply = 0; ply + negamax_search_depth < MAX_PLY; ply++) {
if (ply < min_ply) {
uint32_t n_actions;
action_t *actions = actions_generate(state, &n_actions);
action_to_ptn(actions[rand32() % n_actions], ptn);
} else {
negamax_generate(state);
strcpy(ptn, negamax_ptn);
const pv_leaf_t *leaf = pv_get_leaf();
memcpy(row_head[n_rows], leaf->head, sizeof(row_head[n_rows]));
memcpy(row_cells[n_rows], leaf->cells, sizeof(row_cells[n_rows]));
row_term[n_rows] = leaf->terminal;
row_terminal_value[n_rows] = leaf->terminal_value;
row_game_id[n_rows] = state->current_colour == C_BLACK ? game_id_black : game_id_white;
n_rows++;
}
const enum ACT_RESULT r = do_ptn(state, ptn);
if (r == GAME_END)
break;
if (r != ACT_OK) {
fprintf(stderr, "selfplay: unexpected result %d for ptn %s\n", r,
ptn);
return 0;
}
}
if (n_rows == 0)
return 0;
const uint8_t n_cells = state->board_size * state->board_size;
// final outcome of the played game, from black's perspective
const float R = win_value(state->won);
for (uint32_t k = 0; k < n_rows; k++) {
fprintf(stdout, "%.8f,%.8f,%.8f,", row_head[k][0], row_head[k][1],
row_head[k][2]);
for (uint8_t c = 0; c < n_cells; c++)
fprintf(stdout, "%u,", (unsigned)row_cells[k][c]);
fprintf(stdout, "%u,%.0f,%u,%.0f\n", row_game_id[k], R,
(unsigned)row_term[k], row_terminal_value[k]);
}
return (int)n_rows;
}
const char *usage =
"selfplay, one game of asymmetric self-play training data.\n\n\
Usage: selfplay <size> <depth> <min_ply>\n\n\
Plays a single game of <size>x<size> self-play, with <min_ply> moves made randomly. Only the deterministic plies are written to stdout, one CSV row per recorded search, from ply <min_ply> onwards:\n\n\
\tparity,white_flats,black_flats,<cell ids...>,game_id,R,terminal,leaf_R\n\n\
nn_train.py runs many of these in parallel to generate training data.\n\n\
Copyright (C) 2026, tslil clingman\n\n\
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.";
int main(int argc, char **argv) {
if (argc != 4) {
puts(usage);
return EXIT_FAILURE;
}
const uint8_t size = (uint8_t)atoi(argv[1]);
negamax_search_depth = (uint8_t)atoi(argv[2]);
const uint8_t min_ply = (uint8_t)atoi(argv[3]);
if (size != 5 && size != 6) {
fprintf(stderr, "selfplay: size must be 5 or 6\n");
return EXIT_FAILURE;
}
if (negamax_search_depth > NEGAMAX_MAX_DEPTH) {
fprintf(stderr, "selfplay: depth must be <= %d\n", NEGAMAX_MAX_DEPTH);
return EXIT_FAILURE;
}
if (min_ply >= MAX_PLY) {
fprintf(stderr, "selfplay: min_ply must be < %d\n", MAX_PLY);
return EXIT_FAILURE;
}
// Processes launched in the same second must not share a seed
xors = ((uint64_t)time(NULL) ^ ((uint64_t)getpid() << 32)) *
0x9E3779B97F4A7C15ULL +
0xC0FFEE;
game_id_black = rand32();
game_id_white = rand32();
state = new_tak_state(size);
negamax_init(size);
play_game(min_ply);
negamax_free();
free_tak_state(state);
return EXIT_SUCCESS;
}
|
